System and method for providing a scalable objective metric for automatic video quality evaluation employing interdependent objective metrics

ABSTRACT

There is disclosed an improved system and method for providing a scalable objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. Some of the objective metric model units are independent and some are interdependent. The system determines a scalable objective metric from the plurality of objective metric figures of merit. The scalable objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The system is capable of continually determining a new value of the scalable objective metric as the plurality of objective metric model units receive new video images.

REFERENCE TO PROVISIONAL APPLICATION

This patent application refers to and claims the priority and benefit ofProvisional Patent Application Ser. No. 60/260,842 filed Jan. 10, 2001.

RELATED APPLICATIONS

This patent application is related to co-pending U.S. patent applicationSer. No. 09/734,823 filed Dec. 12, 2000 by Ali et al. entitled “Systemand method for Providing a Scalable Dynamic Objective Metric forAutomatic Video Quality Evaluation.” The present invention is related tothat disclosed in U.S. patent application Ser. No. 09/817,981 filed Mar.27, 2001 by Ali et al. entitled “System and Method for OptimizingControl Parameter Settings in a Chain of Video Processing Algorithms.”Both of the related patent applications are commonly assigned to theassignee of the present invention. The disclosures of both of therelated patent applications are hereby incorporated by reference in thepresent application as if fully set fourth herein.

TECHNICAL FIELD OF THE INVENTION

The present invention is generally directed to systems and methods forevaluating video quality, and, in particular, to an improved system andmethod for providing a scalable objective metric employinginterdependent objective metrics for automatically evaluating videoquality of a video image.

BACKGROUND OF THE INVENTION

Video experts continually seek new algorithms and methods for improvingthe quality of video images. The primary goal is to obtain the mostperceptually appealing video image possible. The ultimate criterion isthe question “How well does the viewer like the resulting picture?” Oneway to answer the question is to have a panel of viewers watch certainvideo sequences and then record the opinions of the viewers concerningthe resulting image quality. The results, however, will vary from panelto panel according to the variability between the viewing panels. Thisproblem is commonly encountered when relying on subjective humanopinion. The severity of the problem is increased when the viewing panelis composed of non-experts.

Results solely based upon on human perception and subjective opinion areusually subjected to subsequent statistical analysis to removeambiguities that result from the non-deterministic nature of subjectiveresults. Linear and non-linear heuristic statistical models have beenproposed to normalize these types of subjective results and obtaincertain figures of merit that represent the goodness (or thedegradation) of video quality. The process of measuring video quality inthis manner is referred to as “subjective video quality assessment.”

Subjective video quality assessment methods give valid indications ofvisible video artifacts. Subjective video quality assessment methods,however, are probabilistic in nature, complex, time consuming, andsometimes difficult to apply. In addition, there is a problem inselecting appropriate viewers for the viewing panel. A non-trainedviewer will be a poor judge of the suitability of new video processingmethods. A non-trained viewer, however, will likely accurately representthe general consumers in the marketplace. On the other hand, a trainedexpert viewer will be overly biased toward detecting minor defects thatwill never be noticed by the general consumer.

To avoid the disadvantages that attend subjective methods for evaluatingvideo quality, it is desirable to use automated objective methods toevaluate video quality. Automated objective methods seek to obtainobjective figure of merits to quantify the goodness (or the degradation)of video quality. The process for obtaining one or more objectivemeasures of the video quality must be automated in order to be able toquickly analyze differing types of video algorithms as the videoalgorithms sequentially appear in a video stream.

Objective measures of video quality are fully deterministic. That is,the results will always be the same when the test is repeated (assumingthe same settings are preserved).

Because the ultimate goal is to present the viewer with the mostappealing picture, a final judge of the value of the objective measuresof video quality is the degree of correlation that the objectivemeasures have with the subjective results. Statistical analysis isusually used to correlate the results objectively obtained(automatically generated) with the results subjectively obtained (fromhuman opinion).

There is a need in the art for improved systems and methods forautomatically measuring video quality. The process of automaticallymeasuring video quality is referred to as “objective video qualityassessment.”

Several different types of algorithms have been proposed that arecapable of providing objective video quality assessment. The algorithmsare generally referred to as “objective video quality models.” A reportfrom the Video Quality Experts Group (VQEG) sets forth and describes theresults of an evaluation performed on ten (10) objective video qualitymodels. The report is dated December 1999 and is entitled “Final Reportfrom the Video Quality Experts Group on the validation of ObjectiveModels of Video Quality Assessment.” The report is presently availableon the World Wide Web at http://www-ext.crc.ca/VQEG.

Each different objective video quality model provides its owndistinctive measurement of video quality referred to as an “objectivemetric.” A “double ended” objective metric is one that evaluates videoquality using a first original video image and a second processed videoimage. A “double ended” objective metric compares the first originalvideo image to the second processed video image to evaluate videoquality by determining changes in the original video image. A “singleended” objective metric is one that evaluates video quality withoutreferring to the original video image. A “single ended” objective metricapplies an algorithm to a video image to evaluate its quality.

No single objective metric has been found to be superior to all theother objective metrics under all conditions and for all videoartifacts. Each objective metric has its own advantages anddisadvantages. Objective metrics differ widely in performance (i.e., howwell their results correlate with subjective quality assessmentresults), and in stability (i.e., how well they handle different typesof video artifacts), and in complexity (i.e., how much computation poweris needed to perform the algorithm calculations).

A wide range of applications exists to which objective metrics may beapplied. For example, fast real-time objective metrics are needed tojudge the quality of a broadcast video signal. On the other hand, morecomplex and reliable objective metrics are better for judging thequality of non-real time video simulations.

Using only one objective metric (and one objective video quality model)limits the evaluation of the quality of a video signal to the level ofevaluation that is obtainable from the objective metric that is used. Itis therefore desirable to use more than one objective metric for videoquality evaluation. An improved system and method that uses more thanone objective metric for video quality evaluation has been disclosed inU.S. patent application Ser. No. 09/734,823 filed Dec. 12, 2000 by Aliet al. entitled “System and Method for Providing a Scalable DynamicObjective Metric for Automatic Video Quality Evaluation.”

There is a need in the art for an improved system and method forcombining objective metrics in order to form more efficient objectivemetrics for video quality evaluation.

SUMMARY OF THE INVENTION

The present invention generally comprises an improved system and methodfor providing a scalable objective metric employing interdependentobjective metrics for automatically evaluating video quality of a videoimage.

In an advantageous embodiment of the present invention, the improvedsystem of the invention comprises an objective metric controller that iscapable of receiving a plurality of objective metric figures of meritfrom a plurality of objective metric model units. The objective metriccontroller is capable of using objective metrics for both desirable andundesirable video image characteristics. The objective metric controlleris also capable of using a plurality of interdependent objectivemetrics. The objective metric controller is capable of determining ascalable objective metric from the plurality of interdependent objectivefigures of merit.

In an advantageous embodiment of the present invention, the improvedmethod of the invention comprises the steps of (1) receiving in anobjective metric controller a plurality of objective metric figures ofmerit from a plurality of objective metric model units comprising atleast one pair of objective metric model units that is interdependent,and (2) determining a scalable objective metric from the plurality ofthe objective metric figures of merit.

It is a primary object of the present invention to provide an improvedsystem and method for providing a scalable objective metric forautomatically evaluating video quality of a video image usinginterdependent object metric model units.

It is another object of the present invention to provide a scalableobjective metric from a correlation factor derived from a mathematicaldescription of an interdependency of at least one interdependent pair ofobjective metric model units.

It is an additional object of the present invention to provide ascalable objective metric from correlation factor derived using a neuralnetwork algorithm that employs both objective quality scores andsubjective quality scores.

It is another object of the present invention to continually determinenew values of the scalable objective metric from new values of theplurality of objective metric figures of merit as new video images arecontinually received.

The foregoing has outlined rather broadly the features and technicaladvantages of the present invention so that those skilled in the art maybetter understand the Detailed Description of the Invention thatfollows. Additional features and advantages of the invention will bedescribed hereinafter that form the subject of the claims of theinvention. Those skilled in the art should appreciate that they mayreadily use the conception and the specific embodiment disclosed as abasis for modifying or designing other structures for carrying out thesame purposes of the present invention. Those skilled in the art shouldalso realize that such equivalent constructions do not depart from thespirit and scope of the invention in its broadest form.

Before undertaking the Detailed Description of the Invention, it may beadvantageous to set forth definitions of certain words and phrases usedthroughout this patent document: the terms “include” and “comprise” andderivatives thereof, mean inclusion without limitation; the term “or,”is inclusive, meaning and/or; the phrases “associated with” and“associated therewith,” as well as derivatives thereof, may mean toinclude, be included within, interconnect with, contain, be containedwithin, connect to or with, couple to or with, be communicable with,cooperate with, interleave, juxtapose, be proximate to, be bound to orwith, have, have a property of, or the like; and the term “controller,”“processor,” or “apparatus” means any device, system or part thereofthat controls at least one operation, such a device may be implementedin hardware, firmware or software, or some combination of at least twoof the same. It should be noted that the functionality associated withany particular controller may be centralized or distributed, whetherlocally or remotely. Definitions for certain words and phrases areprovided throughout this patent document, those of ordinary skill in theart should understand that in many, if not most instances, suchdefinitions apply to prior, as well as future uses of such defined wordsand phrases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, wherein likenumbers designate like objects, and in which:

FIG. 1 is a block diagram that illustrates (1) a plurality of objectivemetric model units for obtaining a plurality of objective metric figuresof merit from a video stream and (2) a objective metric controllercapable of using the plurality of objective metric figures of merit todetermine a scalable objective metric;

FIG. 2 is a flow chart diagram illustrating an advantageous method ofusing a plurality of objective metric figures of merit to determine ascalable objective metric;

FIG. 3 is a flow chart diagram illustrating an advantageous method ofoperation of the improved system and method of the present invention;and

FIG. 4 is a flow chart diagram illustrating an alternative advantageousmethod of operation of the improved system and method of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1 through 4, discussed below, and the various embodiments setforth in this patent document to describe the principles of the improvedsystem and method of the present invention are by way of illustrationonly and should not be construed in any way to limit the scope of theinvention. Those skilled in the art will readily understand that theprinciples of the present invention may also be successfully applied inany type of device for evaluating video quality.

FIG. 1 illustrates system 100 for providing a scalable objective metricfor automatic video quality evaluation. System 100 receives video stream110. Each of a plurality of objective metric model units (120, 130, . .. , 140) receives a copy of the video signal of video stream 110.Objective metric model unit 120 applies a first objective metric model(referred to as “Metric 1”) to obtain a first figure of merit, f(1),that represents the quality of the video signal based on the firstobjective metric model. The first figure of merit, f(1), is provided tocontroller 150.

Similarly, objective metric model unit 130 applies a second objectivemetric model (referred to as “Metric 2”) to obtain a second figure ofmerit, f(2), that represents the quality of the video signal based onthe second objective metric model. The second figure of merit, f(2), isalso provided to controller 150. Continuing in this manner, otherobjective metric model units are added until the last objective metricmodel unit 140 has been added. Objective metric model unit 140 appliesthe last objective metric model (referred to as “Metric N”). Objectivemetric model units (120, 130, . . . , 140) obtain a plurality of figuresof merit (f(1), f(2), . . . , f(N)) and provide them to controller 150.

The figures of merit (f(1), f(2), . . . , f(N)) represent a series of Nevaluations of the quality of the video stream by N different objectivemetrics. The figures of merit (f(1), f(2), . . . , f(N)) may also bedesignated f(i) where the value of i goes from 1 to N.

As will be explained below in greater detail, system 100 of the presentinvention provides a system and method for using the figures of meritf(i) to calculate a scalable objective metric. The letter “F” (shown inFIG. 1) designates the scalable objective metric of the presentinvention.

System 100 of the present invention comprises controller 150 and memory160. Controller 150 may comprise a conventional microprocessor chip orspecially designed hardware. Controller 150 is coupled to a plurality ofobjective metric model units (120, 130, . . . , 140) via signalcommunication lines (shown in FIG. 1). Controller 150 operates inconjunction with an operating system (not shown) located within memory160 to process data, to store data, to retrieve data and to output data.Controller 150 calculates scalable objective metric “F” by executingcomputer instructions stored in memory 160.

Memory 160 may comprise random access memory (RAM), read only memory(ROM), or a combination of random access memory (RAM) and read onlymemory (ROM). In an advantageous embodiment of the present invention,memory 160 may comprise a non-volatile random access memory (RAM), suchas flash memory. Memory 160 may also comprise a mass storage datadevice, such as a hard disk drive (not shown in FIG. 1) or a compactdisk read only memory (CD-ROM) (not shown in FIG. 1).

It is noted that the system and method of the present invention may beused in a wide variety of types of video processing systems, including,without limitation, hard disk drive based television sets and hard diskdrive based video recorders, such as a ReplayTV™ video recorder or aTiVO™ video recorder.

Controller 150 and metric calculation algorithm 170 together comprise anobjective metric controller that is capable of carrying out the presentinvention. Under the direction of computer instructions in metriccalculation algorithm 170 stored within memory 160, controller 150calculates a scalable objective metric “F” using the figures of meritf(i).

A weighting unit 190 within controller 150 dynamically detects thecurrently occurring characteristics of the video sequence. The currentlyoccurring characteristics may include such features as sharpness, color,saturation, motion, and similar types of features. Weighting unit 190assigns a value (or “weight”) w(i) to each objective metric (Metric 1,Metric 2, . . . , Metric N). For example, if Metric 1 is especially goodwhen used on a certain first type of video signal, then the value ofw(1) is given a is greater value than the other values of w(i).Conversely, if Metric 2 is not very good when used on that same firsttype of video signal, then w(2) will be given a lower value than theother values of w(i). If a second type of video signal is present, itmay be that Metric 1 is not as good as Metric 2 when used on the secondtype of video signal. In that case, w(2) is given a higher value andw(1) is given a lower value than the other values of w(i).

Generally speaking, the values of w(i) that weighting unit 190 selectswill vary depending upon the type of video signal that weighing unit 190dynamically detects. Controller 150 uses metric calculation algorithm170 to calculate the sum S of the products of each w(i) and f(i). Thatis,S=w(1)f(1)+w(2)f(2)+ . . . +w(N)f(N)  (1)orS=Σw(i)f(i)  (2)where the value of i runs from 1 to N.

A correlation factor r(i) is associated with each figure of merit f(i).The correlation factor r(i) is obtained from the expression:r(i)=1−[A(i)/B ]  (3)whereA(i)=6Σ[(X(i,j)−Y(i, j)]²  (4)where the value of j runs from 1 to n.and whereB=n(n ²−1)  (5)The values of X(i,j) are the values of a set of n objective data valuesfor a video image. The values of Y(i,j) are the values of a set of nsubjective data values for the same video image. That is, the number ofX data points (n) is the same number of Y data points (n).

The value r(i) is referred to as the “Spearman rank” correlation factor.The value r(i) is a measure of how well the objective X values match thesubjective Y values. The values of the correlations factors r(i) foreach figure of merit f(i) are known, having been previously determinedby statistical analysis. Values of the correlation factors r(i) arestored in metric parameters look up table 180 in memory 150.

A “best fitting” value for scalable objective metric “F” is desired. The“best fitting” value of “F” represents the highest level of correlationof the objective metric measurements of video quality (generatedautomatically) and the subjective measurements of video quality (fromhuman opinions). The “best fitting” value of “F” represents the closestapproximation of the subjective measurements of video quality by theobjective measurements of video quality. Because the video images in avideo stream are constantly changing, the “best fit” will requireconstant automatic updating. The term “dynamic” refers to the ability ofthe objective metric of the present invention to continually change itsvalue to take into account the continual changes of the video images ina video stream.

As previously mentioned, weighting unit 190 continually (i.e.,dynamically) detects the characteristics of the video sequence as theyoccur. For each correlation factor r(i), weighting unit 190 continuallyassigns values of w(i) to each figure of merit f(i). To dynamicallyobtain the “best fitting” value of “F”, metric calculation algorithm 170determines the values of w(i) that cause the value S to be a maximum foreach value of r(i). The largest of these values (i.e., the maximumvalue) is selected to be the scalable objective metric “F.” That is,F=Maximum [S(r(1)), S(r(2)), . . . , S(r(N))]  (6)

Scalable objective metric “F” is referred to as “scalable” because newobjective metric model units can be easily added (as long as theircorrelation factors r(i) are defined). In addition, objective metricmodel units that are no longer desired can easily be removed.

The scalable objective metric “F” of the present invention provides agreat deal of flexibility. For example, for fast (real time) videosignals, any complicated measurement objective metrics may be switchedoff so that their figures of merit are not considered in the metriccalculation process. For simulation and video chain optimizationapplications, where more time can be used to perform the metriccalculation, the more complicated measurement objective metrics may beswitched on so that their figures of merit may be considered in themetric calculation process.

The scalable objective metric of the present invention avoids theshortcomings of any single objective metric. This is because weightingunit 190 will assign a low value to w(i) for any objective metric thatperforms poorly in the presence of a certain set of artifacts. Thescalable objective metric of the present invention achieves the highestcorrelation with the results of subjective testing when compared anysingle objective metric. The scalable objective metric of the presentinvention will be at least as good as the best single objective metricunder all circumstances. Because the scalable objective metric permitsthe inclusion of any objective metric, the system and method of thepresent invention is not limited to use with a particular type ofobjective metric (e.g., a “single ended” objective metric or a “doubleended” objective metric).

It is noted that the elements of the present invention that have beenimplemented in software (e.g., weighting unit 190) may be implemented inhardware if so desired.

As shown in FIG. 1, system 100 of the present invention also comprisesneural network unit 195. In one embodiment neural network unit 195 maybe located within controller 150. The operation of neural network unit195 will be more fully described below.

FIG. 2 is a flow chart diagram illustrating the method of operation ofthe system of the present invention. The steps of the method aregenerally denoted with reference numeral 200. A video image from videostream 110 is provided to N objective metric model units (120, 130, . .. , 140). The N objective metric model units (120, 130, . . . , 140)evaluate the video image and obtain N respective figures of merit, f(i)(step 205).

Weighting unit 190 in objective metric controller 150 then dynamicallydetects video characteristics of the video image and assigns N weights,w(i), to the N figures of merit, f(i) (step 210). For each correlationfactor, r(i), objective metric controller 150 calculates a sum, S(r(i)),that is equal to the sum of each product of weight, w(i), and figure ofmerit, f(i) (step 215).

Objective metric controller 150 then selects the maximum value of thesum, S(r(i)), that corresponds to the best correlation of objectivemeasurements of video quality with subjective measurements of videoquality (step 220). Objective metric controller 150 then assigns thatvalue to be the value of the scalable objective metric “F” (step 225).Objective metric controller 150 then outputs that value of “F” (step230).

After the value of “F” has been output, a determination is made whetherobjective metric controller 150 is still receiving video images(decision step 235). If the video has ended, then the process ends. Ifthe video has not ended and more video images are being received,control passes back to step 205 and the objective controller 150continues to operate in the manner that has been described.

The present invention has been described as a system for providing ascalable objective metric for evaluating video quality of a video image.It is understood that the “scalable objective metric” of the presentinvention is a general case that includes as a subset the more specificcase of providing “static objective metric.” To provide a “staticobjective metric” the present invention 1) receives a plurality ofobjective metric figures of merit from a plurality of objective metricmodel units, and 2) determines a weight value, w(i), for each of theplurality of objective metric figures of merit, and 3) thereafter keepsthe weight values, w(i), constant (i.e., unchanged) during the processof calculating objective metric “F” for video stream 110.

The present invention also comprises a system and method for calculatingan objective metric “F” by using both single objective metrics thatrepresent desired image features and single objective metrics thatrepresent undesired image features. Examples of desired image featuresare sharpness and contrast. Examples of undesired image features arenoise, blockiness, and aliasing. A dynamic objective metric “F” thatproduces good results may be obtained by using competing singleobjective metrics. That is, single objective metrics that representingboth desired and undesired image features are to be combined.

The single objective metrics may be interdependent. For example,consider a simple sharpness objective metric that is dependent on thepresence of noise in the image. Let the sharpness of an image berepresented by the signal power P_(H) in a high frequency band B_(H).Enhancing the sharpness of the image will increase the signal power inthis frequency band to P_(H′) where P_(H′) is equal to P_(H) plus thechange in P_(H) (i.e., ΔP_(H)). This may be expressed as:P _(H′) =P _(H) +ΔP _(H)  (7)

The measured signal power is an indication of the image sharpness.

Adding white noise to the clean image will also increase the signalpower to:P _(H″) =P _(H) +N _(H)  (8)Where N_(H) is the noise power in frequency band B_(H). The sharpnessmetric should therefore be defined as the total signal power P_(H) minusthe measured noise power N_(H). The sharpness metric is interdependenton the noise metric.

If single objective metrics are used that are not interdependent, thenscalable objective metric “F” is calculated as in Equation (6). Theweight factors that are assigned to desired features are given theopposite sign of weight factors that are assigned to undesired features.

If single objective metrics are used that are interdependent, thenscalable objective metric “F” is not necessarily a linear function ofthe values of the single objective metrics. When interdependent singleobjective metrics are present the value of the scalable objective metric“F” may be determined by (1) describing the interdependencies withmathematical equations, and (2) correlating the images that correspondto the interdependencies with subjective quality scores.

Alternatively, the value of the scalable objective metric “F” may bedetermined by using a neural network algorithm that employs bothobjective quality scores and subjective quality scores. In thisembodiment of the invention, controller 150 employs neural network unit195 to calculate a value of the scalable objective metric “F” from thevalues of the interdependent objective metrics. In one embodiment,neural network unit 195 is located within controller 150. In otherembodiments, neural network unit 195 may be located externally tocontroller 150.

FIG. 3 is a flow chart diagram illustrating an alternate method ofoperation of the system of the present invention. The steps of themethod are generally denoted with reference numeral 300. A video imagefrom video stream 110 is provided to N objective metric model units(120, 130, . . . , 140). The N objective metric model units (120, 130, .. . , 140) evaluate the video image and obtain N respective figures ofmerit, f(i) (step 305).

Weighting unit 190 in objective metric controller 150 then dynamicallydetects video characteristics of the video image and assigns N weights,w(i), to the N figures of merit, f(i) (step 310). For independent (i.e.,non-interdependent) objective metrics, objective metric controller 150calculates a sum, S(r(i)), using a correlation factor, r(i). The sum,S(r(i)), is equal to the sum of each product of weight, w(i), and figureof merit, f(i) (step 315).

For independent (i.e., non-interdependent) objective metrics, objectivemetric controller 150 then selects the maximum value of the sum,S(r(i)), that corresponds to the best correlation of objectivemeasurements of video quality with subjective measurements of videoquality (step 320). Objective metric controller 150 then assigns thatvalue to be the value of the scalable objective metric “F” (step 325).

For interdependent objective metrics, objective metric controller 150calculates a value of the scalable objective metric “F” from amathematical description of the interdependencies of the interdependentobjective metrics (Step 330).

Objective metric controller 150 then outputs the value of “F” (step335). After the value of “F” has been output, a determination is madewhether objective metric controller 150 is still receiving video images(decision step 340). If the video has ended, then the process ends. Ifthe video has not ended and more video images are being received,control passes back to step 305 and the objective controller 150continues to operate in the manner that has been described.

FIG. 4 is a flow chart diagram illustrating an alternate method ofoperation of the system of the present invention. The steps of themethod are generally denoted with reference numeral 400. A video imagefrom video stream 110 is provided to N objective metric model units(120, 130, . . . , 140). The N objective metric model units (120, 130, .. . , 140) evaluate the video image and obtain N respective figures ofmerit, f(i) (step 410).

For independent or interdependent objective metrics, objective metriccontroller 150 uses neural network unit 195 to calculate a value of thescalable objective metric “F” from the values of the interdependentobjective metrics (Step 420). The neural network algorithm in neuralnetwork unit 195 has previously been trained with subjective videoquality scores.

Objective metric controller 150 then outputs the value of “F” (step430). After the value of “F” has been output, a determination is madewhether objective metric controller 150 is still receiving video images(decision step 440). If the video has ended, then the process ends. Ifthe video has not ended and more video images are being received,control passes back to step 410 and the objective controller 150continues to operate in the manner that has been described.

Although the present invention has been described in detail, thoseskilled in the art should understand that they can make various changes,substitutions and alterations herein without departing from the spiritand scope of the invention in its broadest form.

1. A system for providing a scalable objective metric for evaluatingvideo quality of a video image, said system comprising: an objectivemetric controller capable of receiving a plurality of objective metricfigures of merit from a plurality of objective metric model units, andcapable of determining said scalable objective metric from saidplurality of objective metric figures of merit, wherein at least onepair of said plurality of metric model units is interdependent.
 2. Thesystem for providing a scalable objective metric for evaluating videoquality of a video image as claimed in claim 1 wherein the number ofsaid plurality of objective metric figures of merit may vary from two toN, where N is an integer number.
 3. The system for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 1 wherein said objective metric controller is capableof determining said scalable objective metric from a correlation factorderived from a mathematical description of an interdependency of said atleast one interdependent pair of said plurality of metric model units.4. The system for providing a scalable objective metric for evaluatingvideo quality of a video image as claimed in claim 1 wherein saidobjective metric controller is capable of determining said scalableobjective metric from a correlation factor derived using a neuralnetwork algorithm that employs both objective quality scores andsubjective quality scores.
 5. The system for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 1 comprising a weighting unit that assigns weightvalues to each of a plurality of non-interdependent objective metricfigures of merit by using a correlation factor, r(i), for each of saidobjective metric figures of merit, where each correlation factor, r(i),for an objective metric figure of merit represents how well theobjective metric figure of merit evaluates video image characteristics.6. The system for providing a scalable objective metric for evaluatingvideo quality of a video image as claimed in claim 1 wherein saidplurality of objective metric model units comprises at least oneobjective metric model unit for a desirable video image feature and atleast one objective metric model unit for an undesirable video imagefeature.
 7. The system for providing a scalable objective metric forevaluating video quality of a video image as claimed in claim 5 whereinsaid objective metric controller calculates a value, F, for saidscalable objective metric from interdependent objective metrics using amathematical description of interdependencies of said interdependentobjective metrics.
 8. The system for providing a scalable objectivemetric for evaluating video quality of a video image as claimed in claim5 wherein said objective metric controller is capable of calculating aplurality of sums for a plurality of non-interdependent objectivemetrics where each sum, S(r(i)), is equal to the sum of each product ofweight value, w(i), and figure of merit, f(i), for each of saidcorrelation factors, r(i).
 9. The system for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 8 wherein said objective metric controller is capableof obtaining said scalable objective metric by selecting said scalableobjective metric to be the maximum value of the plurality of sums,S(r(i)), where said maximum value represents the best correlation ofobjective metric measurements of said video image with subjectivemeasurements of said video image.
 10. The system for providing ascalable objective metric for evaluating video quality of a video imageas claimed in claim 1 wherein said objective metric controller iscapable of continually determining a new value of said scalableobjective metric from new values of said plurality of objective figuresof merit as said plurality of objective metric model units continuallyreceive new video images.
 11. The system for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 1 wherein said objective metric controller is capableof adding at least one objective metric to said plurality of objectivefigures of merit, and wherein said objective metric controller iscapable of deleting at least one objective metric from said plurality ofobjective figures of merit.
 12. The system for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 1 wherein said objective metric controller comprises: acontroller capable of receiving a plurality of objective metric figuresof merit, f(i), from a plurality of objective metric model units; and ametric calculation algorithm contained within a memory coupled to saidcontroller, said metric calculation algorithm containing instructionscapable of being executed by said controller to determine a value, F,for said scalable objective metric from a weighted average of saidplurality of objective metric figures of merit, f(i), wherein at leastone pair of said plurality of objective metric model units isinterdependent.
 13. The system for providing a scalable objective metricfor evaluating video quality of a video image as claimed in claim 1comprising: a plurality of objective metric model units wherein at leastone pair of said plurality of objective metric model units isinterdependent; an objective metric controller capable of receiving aplurality of objective metric figures of merit from said plurality ofobjective metric model units, wherein said objective metric controlleris capable of determining a value, F, for said scalable objective metricfrom a plurality of non-interdependent objective metric figures ofmerit, f(i), and capable of determining a value, F, for said scalableobjective metric from at least two interdependent objective metrics,wherein said value F represents an objective metric that represents amaximum level of correlation of objective metric measurements of videoquality and subjective measurements of video quality.
 14. A method forproviding a scalable objective metric for evaluating video quality of avideo image comprising the steps of: receiving in an objective metriccontroller a plurality of objective metric figures of merit from aplurality of objective metric model units wherein at least one pair ofsaid plurality of objective metric model units is interdependent; anddetermining said scalable objective metric from said plurality of saidobjective metric figures of merit.
 15. The method for providing ascalable objective metric for evaluating video quality of a video imageas claimed in claim 14 wherein the step of determining said scalableobjective metric from said plurality of said objective metric figures ofmerit comprises the step of: determining said scalable objective metricfrom a correlation factor derived from a mathematical description of aninterdependency of said at least one interdependent pair of saidplurality of said objective metric model units.
 16. The method forproviding a scalable objective metric for evaluating video quality of avideo image as claimed in claim 14 wherein the step of determining saidscalable objective metric from said plurality of said objective metricfigures of merit comprises the step of: determining said scalableobjective metric from a correlation factor derived using a neuralnetwork algorithm that employs both objective quality sources andsubjective quality sources.
 17. The method for providing a scalableobjective metric for evaluating video quality of a video image asclaimed in claim 14 further comprising the steps of: assigning weightvalues to each of said plurality of objective metric figures of merit byusing a correlation factor, r(i), for each of a plurality ofnon-interdependent objective metric figures of merit, where eachcorrelation factor, r(i), for an objective metric figure of meritrepresents how well the objective metric figure of merit evaluates videoimage characteristics.
 18. The method for providing a scalable objectivemetric for evaluating video quality of a video image as claimed in claim14 wherein said plurality of objective metric model units comprises atleast one objective metric model unit for a desirable video imagefeature and at least one objective metric model unit for an undesirablevideo image feature.
 19. The method for providing a scalable objectivemetric for evaluating video quality of a video image as claimed in claim14 further comprising the steps of: receiving in said objective metriccontroller new values of said plurality of objective metric figures ofmerit from said plurality of objective metric model units as saidplurality of objective metric model units receive new video images; andcontinually determining a new value of said scalable objective metricfrom said new values of said plurality of objective metric figures ofmerit.
 20. The method for providing a scalable objective metric forevaluating video quality of a video image as claimed in claim 14 furthercomprising the steps of: determining a weight value, w(i), for each ofsaid plurality of objective metric figures of merit; keeping said weightvalues constant; and calculating said scalable objective metric usingsaid constant weight values.
 21. A method for providing a scalableobjective metric for evaluating video quality of a video imagecomprising the steps of: receiving in an objective metric controller aplurality of objective metric figures of merit from a plurality ofobjective metric model units wherein each of said plurality of objectivemetric model units is independent; and determining said scalableobjective metric from said plurality of said objective metric figures ofmerit from a correlation factor derived using a neural network algorithmthat employs both objective quality sources and subjective qualitysources.